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This paper addresses unsupervised discovery and localization of dominant objects from a noisy collection of images or videos. The setting of this problem is fully unsupervised, without even class labels or any assumption of a single dominant class, and thus far more general than those of typical colocalization or weakly-supervised localization tasks. Interestingly, our approach also discovers the...
Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process is sidestepped in weakly supervised learning. In this case, the supervised information is restricted to binary labels that indicate the absence/presence of object instances in the image, without their locations...
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